Gene Finding BCH 339 N Systems Biology Bioinformatics

Gene Finding BCH 339 N Systems Biology / Bioinformatics – Spring 2016 Edward Marcotte, Univ of Texas at Austin

Lots of genes in every genome Do humans really have the biggest genomes? Nature Reviews Genetics 13: 329 -342 (2012)

Lots of genes in every genome animal 6. 5 Gb (2 X human) 17 K genes gigabases (not gigabytes)

Where are the genes? How can we find them? GATCACTTGATAAATGGGCTGAAGTAACTCGCCCAGATGAGGAGTGTGCTGCCTCCAGA ATCCAAACAGGCCCACTAGGCCCGAGACACCTTGTCTCAGATGAAACTTTGGACTCGGA ATTTTGAGTTAATGCCGGAATGAGTTCAGACTTTGGGGGACTGTTGGGAAGGCATGATT GGTTTCAAAATGTGAGAAGGACATGAGATTTGGGAGGGGCTGGGGGCAGAATGATATA GTTTGGCTCTGCGTCCCCAATCTCATGTCAAATTGTAATCCTCATGTGTCAGGGGA GAGGCCTGGTGGGATGTGATTGGATCATGGGAGTGGATTTCCCTCTTGCAGTTCTCGTG ATAGTGAGTTCTCACGAGATCTGGTTGTTTGAAAGTGTGCAGCTCCTCCCCCTTCG CGCTCTCTCCCCTGCTCCACCATGGTGAGACGTGCTTGCGTCCCCTTTGCCTTCTGCC ATGATTGTAAGCTTCCTCAGGCGTCCTAGCCACGCTTCCTGTACAGCCTGAGGAACTGGG AGTCAATGAAACCTCTTCATAAATTACCCAGTTTCAGGTAGTTCTAGCAGTG TGATAATGGACGATACAAGTAGAGACTGAGATCAATAGCATTTGCACTGGGCCTGGAAC ACACTGTTAAGAACGTAAGAGCTATTGCTGTCATTAGTAATATTCTGTATTATTGGCAACA TCATCACAATACACTGCTGTGGGAGGGTCTGAGATACTTCTTTGCAGACTCCAATATTTG TCAAAACATAAAATCAGGAGCCTCATGAATAGTGTTTAAATTTTTACATAATAATACATTG CACCATTTGGTATATGAGTCTTTTTGAAATGGTATATGCAGGACGGTTTCCTAATATACA GAATCAGGTACACCTCCTCTTCCATCAGTGCGTGAGTGTGAGGGATTGAATTCCTCTGGT TAGGAGTTAGCTGGGGGTTCTACTGCTGTTGTTACCCACAGTGCACCTCAGACTCA CGTTTCTCCAGCAATGAGCTCCTGTTCCCTGCACTTAGAGAAGTCAGCCCGGGGACCAGA CGGTTCTCTCCTCTTGCCTGCTCCAGCCTTGGCCTTCAGCAGTCTGGATGCCTATGACACA GAGGGCATCCTCCCCAAGCCCTGGTCCTTCTGTGAGTGGTGAGTTGCTGTTAATCCAAAA GGACAGGTGAAAACATGAAAGCC…

A toy HMM for 5′ splice site recognition (from Remember Sean Eddy’s NBT primer this? linked on the course web page)

Let’s start with prokaryotic genes What elements should we build into an HMM to find bacterial genes?

Let’s start with prokaryotic genes Can be polycistronic: http: //nitro. biosci. arizona. edu/courses/EEB 600 A-2003/lectures/lecture 24. html

Remember this? A Cp. G island model might look like: ( of course, need the parameters, but maybe these are the most important…. ) A T C G A p(C G) is higher Cp. G island model T C G p(C G) is lower Not Cp. G island model P( X | Cp. G island) Could calculate P( X | not Cp. G island) (or log ratio) along a sliding window, just like the fair/biased coin test

One way to build a minimal gene finding Markov model A T C G A Transition probabilities reflect codons Coding DNA model T C G Transition probabilities reflect intergenic DNA Intergenic DNA model P( X | coding) Could calculate P( X | not coding) (or log ratio) along a sliding window, just like the fair/biased coin test

Really, we’ll want to detect codons. The usual trick is to use a higher-order Markov process. A standard Markov process only considers the current position in calculating transition probabilities. An nth-order Markov process takes into account the past n nucleotides, e. g. as for a 5 th order: Codon 1 Codon 2 Image from Curr Op Struct Biol 8: 346 -354 (1998)

5 th order Markov chain, using models of coding vs. non-coding using the classic algorithm Gen. Mark 1 st reading frame Direct strand 2 nd reading frame 3 rd reading frame 1 st reading frame Complementary (reverse) strand 2 nd reading frame 3 rd reading frame

An HMM version of Gen. Mark

For example, accounting for variation in start codons…

… and variation in gene lengths Length distributions (in # of nucleotides) Coding (ORFs) Non-coding (intergenic)

(Placing these curves on top of each other) Short ORFS occur often by chance Coding (ORFs) Non-coding (intergenic) Protein-coding genes <100 aa’s are hard to find Long ORFS tend to be real protein coding genes

Model for a ribosome binding site (based on ~300 known RBS’s)

How well does it do on well-characterized genomes? But this was a long time ago!

Eukaryotic genes What elements should we build into an HMM to find eukaryotic genes?

Eukaryotic genes http: //greatneck. k 12. ny. us/GNPS/SHS/dept/science/krauz/bio_h/Biology_Handouts_Diagrams_Videos. htm

We’ll look at the Gen. Scan eukaryotic gene annotation model:

We’ll look at the Gen. Scan eukaryotic gene annotation model: Zoomed in on the forward strand model…

Introns and different flavors of exons all have different typical lengths Introns Internal exons Initial exons Terminal exons

Taking into account donor splice sites

An example of an annotated gene…

Algorithm predicts: Negative Positive How well do these programs work? We can measure how well an algorithm works using these: True answer: Positive Negative True positive False negative True negative Specificity = TP / (TP + FP) Sensitivity = TP / (TP + FN) Nature Reviews Genetics 13: 329 -342 (2012)

How well do these programs work? How good are our current gene models? Nature Reviews Genetics 13: 329 -342 (2012)

GENSCAN, when it was first developed…. Accuracy per base Accuracy per exon

In general, we can do better with more data, such as m. RNA and conservation Nature Reviews Genetics 13: 329 -342 (2012)

How well do we know the genes now? In the year 2000 = scientists from around the world held a contest (“GASP”) to predict genes in part of the fly genome, then compare them to experimentally determined “truth” Genome Research 10: 483– 501 (2000)

How well do we know the genes now? In the year 2000 “Over 95% of the coding nucleotides … were correctly identified by the majority of the gene finders. ” “…the correct intron/exon structures were predicted for >40% of the genes. ” Most promoters were missed; many were wrong. “Integrating gene finding and c. DNA/EST alignments with promoter predictions decreases the number of false-positive classifications but discovers less than one-third of the promoters in the region. ” Genome Research 10: 483– 501 (2000)

How well do we know the genes now? In the year 2006 = scientists from around the world held a contest (“EGASP”) to predict genes in part of the human genome, then compare them to experimentally determined “truth” We 18 groups 36 programs discussed these earlier

Transcripts vs. genes

In the year 2006 So how did they do? • “The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. ” • “…taking into account alternative splicing, … only approximately 40% to 50% accuracy. • At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. ”

At the gene level, most genes have errors In the year 2006

How well do we know the genes now? In the year 2008 = scientists from around the world held a contest (“NGASP”) to predict genes in part of the worm genome, then compare them to experimentally determined “truth” • 17 groups from around the world competed • “Median gene level sensitivity … was 78%” • “their specificity was 42%”, comparable to human

For example: In the year 2008 Confirmed ? ?

How well do we know the genes now? In the year 2012 = a large consortium of scientists trying to annotate the human genome using a combination of experiment and prediction. Best estimate of the current state of human genes.

How well do we know the genes now? In the year 2012 Quality of evidence used to support automatic, manually, and merged annotated transcripts (probably reflective of transcript quality) 23, 855 transcripts 89, 669 transcripts 22, 535 transcripts

How well do we know the genes now? In the year 2015 The bottom line: • Gene prediction and annotation are hard • Annotations for all organisms are still buggy • Few genes are 100% correct; expect multiple errors per gene • Most organisms’ gene annotations are probably much worse than for humans

The Univ of California Santa Cruz genome browser